The Best Python Books for Data Science

Python was first released in 1991, so it has been around for a long while. However, it has gained much of its popularity in recent years. The use of Python in data science has been the most influential factor in its proliferation.

According to the Popularity of Programming Language Index (PYPL Index), Python is currently the most popular language, and it grew the most in the last 5 years. The PYPL Index is created by analyzing how often language tutorials are searched on Google.

There are two main reasons why Python is the most preferred language among aspiring data scientists and people who work in the field of data science.

The first is that Python is easy to learn. Its syntax is clear, intuitive, and highly readable. Since people from various technical and non-technical backgrounds work in the data science ecosystem, a programming language that is not difficult to learn is likely to be their first choice.

The second reason is the numerous, extremely helpful Python libraries. These libraries simplify and expedite most of the tasks in data science, from data cleaning to creating machine learning models. If you’d like to learn more about these libraries, I highly recommend reading this article about the top 15 Python libraries for data science.

If you’d like to learn more about what data scientists do and what they use Python for, here is a great article that answers these questions in detail.

The most efficient way of learning Python, or any other programming language or software tool, is through interactive online courses. They allow for practicing while explaining topics and concepts. This combination is fundamental to learning.

Data science books can be used as supplementary learning materials to online courses. So far, two articles about the best Python books have been published on the LearnPyhon.com blog: The Best Python Books and The Best Python Books, Part 2.

In this article, we narrow our focus to review the best Python books for data science. As a data scientist who has been actively learning it for over 3 years, I have made my selections based on my own experience and what I have learned from the data science community.

Each book title is linked to its Amazon page so that you can find it easily. It is important to note Amazon has had no impact on the selection, nor do we receive any compensation from linking to the Amazon listings.

This is an introductory book that helps you get started with your data science journey in Python. It starts by explaining the close relation between Python and data science. The author also explains the advantages of using Python to learn data science.

There is a chapter that reviews the Python basics, which is very helpful if you are new to Python and programming. For this reason, you should be fine even if you do not have any prior experience with Python.

Then, several chapters explain how to clean, manipulate, and organize data. You will also have a chance to learn about data visualization with Matplotlib.

The book includes chapters about data analysis and machine learning as well.

This data science book covers the most common tasks such as data manipulation, data visualization, and machine learning. The author, the Director of Open Software at the University of Washington’s eScience Institute, explains the topics and concepts clearly by providing worked-through examples. You will have the opportunity to study the most widely-used Python libraries in data science: NumPy, Pandas, Matplotlib, and Scikit-Learn.

If you are new to data science and these libraries, I suggest starting with a more beginner-friendly book. This book can be your second or third one, as it quickly moves to more complex tasks such as array broadcasting, vectorized operations, customizing plots, and so on. However, once you are comfortable with the basics, this data science book is a great resource for learning advanced functionalities of the Python data science libraries.

This data science book by Joel Grus, a software engineer and data scientist, is a great resource for understanding the fundamental algorithms used in data science.

We sometimes use algorithms without having a comprehensive understanding of how they work. The libraries allow for implementing commonly used algorithms with a few lines of code, which is great as it saves us from writing several lines of code.

However, we also need to learn what goes on under the hood. This book demonstrates how to implement such algorithms from scratch, which is quite helpful in understanding them. It also helps you learn the pros and cons of the algorithms.

Model creation is an iterative process that requires evaluating, tuning, and adjusting your model several times. Therefore, it is very important to have a good understanding of these algorithms to perform a robust and accurate evaluation. This book has sections for gradient descent, linear regression, and decision trees, and other algorithms used by data scientists to create machine learning models. It also has sections for linear algebra, statistics, and probability, which are essential for data science.

Machine learning is a subfield of data science with a wide range of applications such as demand forecasting, predictive maintenance, inventory optimization in retail, customer churn prediction, targeted marketing through customer segmentation, and image classification, among others.

In this data science book, the authors explain the fundamental concepts and applications of machine learning. They also evaluate commonly used machine learning algorithms in terms of their advantages and shortcomings.

This book focuses on the practical side rather than providing in-depth theoretical knowledge. You learn the necessary steps to create a machine learning application using Python libraries.

You also find highly useful information on evaluation and parameter tuning as well. These activities require a substantial effort in creating machine learning models, and you may have to do several trials before the model is ready to be deployed in production.

This book is an outstanding resource for anyone who plans to understand and perform machine learning or deep learning. According to Elon Musk, this is the only comprehensive book on the subject.

It was written by the pioneers in the field of data science. For one, Ian Goodfellow is the creator of the generative adversarial network (GAN), a type of neural network mainly used for generative modeling.

It is safe to say this is not a beginner-friendly book especially if you do not have a technical background. It takes time to absorb and understand the concepts explained in the book. They include probability and information theory, optimization algorithms, convolutional networks, and natural language processing, among others.

If you plan to work with machine learning and deep learning, you should have a comprehensive understanding of the concepts covered in this book.

The author is currently working as a researcher at Google. He is the creator of Keras, a deep learning framework built on top of TensorFlow. Keras is widely used by practitioners in deep learning and machine learning.

In addition to the conceptual and theoretical information, the book contains lots of examples, which is very helpful for the learning process. It is designed for both novice and experienced machine learning practitioners.

After an introduction to deep learning, the book covers common deep learning applications such as image classification and generation, time series forecasting, and text classification and generation.

Compared to the previous book, this one is heavier on the practical side. I recommend reading both because theoretical knowledge is just as important as hands-on experience.

Data science books are great resources for learning. But they do not replace interactive online courses. When learning a software tool or package, what makes learning permanent and long-lasting is practice.

LearnPython.com offers several interactive online courses that allow for practicing while learning. Python Basics track is a great start for your journey with Python. If you plan to work in data science, you should then complete the Python for Data Science track.